Reward-Free Continual Adaptation for Resilient Space Robots

2026-08-24

AI Summary

为解决太空机器人在无奖励信号情况下适应硬件退化问题，提出一种基于潜在状态世界模型的无奖励持续学习框架。

Abstract

Space robots operate in extreme environments where hardware degradation can critically compromise traditional control strategies. While continual reinforcement learning offers a promising mechanism for online adaptation, it inherently requires access to a reward signal during deployment. However, precise reward computation in space is often infeasible due to the lack of external tracking systems and the overall complexity of the environment. To address the challenge of unobservable rewards, we introduce a reward-free continual learning framework that leverages latent-state world models. By pre-training a model-based agent across diverse simulations, the world model learns a robust predictor of the reward structure within its latent space. Upon deployment to an environment with severe hardware degradation, we freeze the observation encoder and reward predictor to update only the transition dynamics of the world model through unsupervised rollouts. By training the policy entirely on imagined trajectories generated by this updated world model, the agent adapts to altered dynamics without receiving new rewards. We demonstrate our approach across simulated planetary traversal, orbital navigation, and precision assembly tasks subjected to severe morphological failures.

Problem

• Space Robots
• Hardware Degradation
• Reward-Free Learning
• Continual Adaptation
• Latent-State World Models

Innovation

• reward-free continual learning
• latent-state world models
• unsupervised rollouts
• imagined trajectories